Instructions to use GYR1-determine/llmagent4incident-response with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GYR1-determine/llmagent4incident-response with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-14B")
model = PeftModel.from_pretrained(base_model, "GYR1-determine/llmagent4incident-response")

Transformers

How to use GYR1-determine/llmagent4incident-response with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GYR1-determine/llmagent4incident-response")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("GYR1-determine/llmagent4incident-response", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GYR1-determine/llmagent4incident-response with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GYR1-determine/llmagent4incident-response"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GYR1-determine/llmagent4incident-response",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GYR1-determine/llmagent4incident-response

SGLang

How to use GYR1-determine/llmagent4incident-response with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GYR1-determine/llmagent4incident-response" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GYR1-determine/llmagent4incident-response",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GYR1-determine/llmagent4incident-response" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GYR1-determine/llmagent4incident-response",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GYR1-determine/llmagent4incident-response with Docker Model Runner:
```
docker model run hf.co/GYR1-determine/llmagent4incident-response
```

LLM Agent for Incident Response

This repository contains the LoRA adapter weights of a language model fine-tuned for incident-response planning and recovery-action generation.

Base Model

This adapter must be loaded on top of:

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

Adapter Details

Fine-tuning method: LoRA
Task type: Causal language modeling
LoRA rank: 64
LoRA alpha: 128
LoRA dropout: 0.05
Target modules: q_proj, v_proj
Released checkpoint: 850

Repository Files

adapter_model.safetensors: LoRA adapter weights
adapter_config.json: LoRA adapter configuration
README.md: model documentation

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
adapter_name = "GYR1-determine/llmagent4incident-response"

tokenizer = AutoTokenizer.from_pretrained(base_model_name)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
)

model = PeftModel.from_pretrained(base_model, adapter_name)

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Model tree for GYR1-determine/llmagent4incident-response

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

Adapter

(115)

this model